This disclosure relates generally to a predictive modeling technique for a multi-cell battery. More specifically, the disclosure relates to testing and analyzing the one or more characteristics of the battery at an individual cell level in a multi-cell architecture.
A battery is a device placed in communication with an electronic machine and functions to supply the machine with electrical energy. The use and evolution of batteries has grown with the use of complex electronic devices. Today, a battery pack with multiple cells is provided to power complex electronic devices. For example, with respect to hybrid and battery operated vehicles, battery packs are known to consist of hundreds or thousands of individual lithium-ion cells within the pack.
Accurate battery pack testing is crucial to indicate the health of a battery pack and predict life performance. When it is indicated that a battery pack is not healthy, the majority of the cells may in fact be healthy, but perhaps one or more unhealthy cells are contributing to the flag indication. This can result in inaccurate cell health estimates and inaccurate prediction of life performance of the battery pack. Accordingly, the health of the battery pack may directly correspond to the health of the individual cells. To remedy this problem, data-based assumptions or algorithmic inferences may be made for each cell's health. These remedies are based on testing the battery pack as a single entity.